There has been notable progress in the development of fully autonomous automobiles capable of navigating through traffic without human intervention. For example, systems may use sensing technology to warn drivers of impending collisions, or even take control of a vehicle in certain situations where the driver either ignores or cannot heed a warning. While the autonomous vehicle will probably exist at some time, existing vehicle systems still have many challenges to overcome in order to be practical.
Some collision warning systems use a radar-based detector, combined with a vision-based land-sensing module, to detect and track vehicles ahead of the host vehicle. The radar-based system monitors the moving pattern of all objects tracked by the radar sensor to determine potential threats along the host's path. The host's path is provided by the lane module. The radar sensor has a limitation in that it not only detects moving and stationary vehicles, but also many fixed roadway infrastructures, such as overpasses and overhead signs. A collision warning system that provides frequent false alarms can be a nuisance rather than a help to drivers.
Typical radar sensors are designed to have a small vertical field of view (VFOV)˜5 degrees in an effort to avoid detection of overhead objects. Even at a maximum detection range of 120 meters, some overhead objects are still routinely detected. There are a number of possible explanations for the false alarms, including, misalignment of the radar axis relative to the ground, often as a result of the radar transmitter being aimed too high. Other factors include ground reflections, which can create “ghosting”. Additional possible sources of error include radar side lobes, and certain types of terrain. The terrain based error sources can occur because overhead objects may actually be within the vertical field of view due to the slope of the road. Therefore redesigning a radar sensor to provide a narrower vertical beam will not likely be successful in solving the problem completely. Additionally, many of these solutions could result in a less robust system that might miss actual obstructions and may still generate false alarms. Therefore there is a need for a robust system that effectively warns users when there is a potential for collision and simultaneously minimizes or eliminates the number of false alarms.